Group Spike and Slab Variational Bayes
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In this manuscript we introduce Group Spike-and-slab Variational Bayes (GSVB), a scalable method for group sparse regression. We constrcut a fast co-ordinate ascent variational inference algorithm for several model families including: the Gaussian, Binomail and Poisson. Through extensive numerical studies we demonstrate that GSVB provides state-of-the-art performance, offering a computationally inexpensive substitute to MCMC, whilst performing comparably or better than existing MAP methods. Additionally, we analyze three real world datasets wherein we highlight the practical utility of our method, demonstrating that GSVB provides parsimonious models with excellent predictive performance, variable selection and uncertainty quantification.
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